To adapt to the dynamic environment, self-adaptive systems are proposed, which have been extensively studied in many works [1]}, [2]}, [3]}. Self-adaptive methods can be utilized in recognition systems to improve the performance from the perspective of model structure. For example, B. Freni et al. [4]} and G. Orrù et al. [5]} designed self-updating methods for template-based face recognition systems. A. Mhenni et al. [6]} proposed a novel user-dependent template update strategy for keystroke recognition.
Most of methods require adequate samples before updating strategies are carried out. In a real person identification system, samples are always produced in a streaming manner, so that updating the model after data accumulation is time-consuming. To achieve the real information of the current data, we borrowed the idea from the human-in-the-loop systems [7]}, [8]}, which arranged humans to annotate samples for obtaining or updating a training set. Active learning is a classical learning paradigm leveraged in human-in-the-loop systems.
The most typical applications are computer vision, machine translation, outlier detection [9]}, [10]}, [11]}, [12]}, etc. The difference is that we set the human in both training and test process for model adaptation other than training set adaptation. In this way, our method can timely adjust the identification system only by existing and current data without accumulation of the new data.
